Gemma 3 27B QAT Fidelity Regression: Why Attention Layers Need More Than Q4_0
dampflokfreund · reddit · 2026-08-07
A developer extensively compared the QAT (Quantization-Aware Training) version of Gemma 3 27B against the traditional Q4KL quantization, finding that while QAT reduces memory, it causes regressions in high-precision tasks like coding and long-context creative writing.
- Precision Difference: Traditional Q4K strategies retain Q80 precision for token embeddings and attention layers, minimizing error accumulation in long contexts.
- QAT Limitation: The QAT model provided by Google quantizes these crucial layers down to Q40.
- Conclusion: The author suggests that if Google aligned the QAT model to the modern Q4K format instead of the older Q40, it would significantly improve the model's fidelity for complex tasks.
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